> Sounds good to me. I agree that we should prioritize within-numpy
> consistency over consistency with Python.
>
I agree with that. Because of numpy consitetncy, the `**` operator should
always return float. Right now the case is:
>>> aa = np.arange(2, 10, dtype=int)
array([2, 3, 4, 5, 6, 7, 8, 9])
>>> bb = np.linspace(0, 7, 8, dtype=int)
array([0, 1, 2, 3, 4, 5, 6, 7])
>>> 1/aa
array([ 0.5 , 0.33333333, 0.25 , 0.2 , 0.16666667,
0.14285714, 0.125 , 0.11111111])
>>> aa**-1
array([0, 0, 0, 0, 0, 0, 0, 0])
>>> 1/aa**2
array([ 0.25 , 0.11111111, 0.0625 , 0.04 , 0.02777778,
0.02040816, 0.015625 , 0.01234568])
>>> aa**-2
array([0, 0, 0, 0, 0, 0, 0, 0])
>>> aa**bb
array([ 1, 3, 16, 125, 1296, 16807, 262144,
4782969])
>>> 1/aa**bb
array([ 1.00000000e+00, 3.33333333e-01, 6.25000000e-02,
8.00000000e-03, 7.71604938e-04, 5.94990183e-05,
3.81469727e-06, 2.09075158e-07])
>>> aa**(-bb)
array([1, 0, 0, 0, 0, 0, 0, 0])
For me this behaviour is confusing. But I am not an expert just a user. I
can live together with anything if I know what to expect. And I greatly
appreciate the work of any developer for this excellent package.
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